https://github.com/nishkarshraj/100daysofmlcode
My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
https://github.com/nishkarshraj/100daysofmlcode
100daysofcode 100daysofmlcode artificial-intelligence artificial-neural-networks big-data classification classification-algorithm deep-learning generative-ai hacktoberfest linear-algebra linear-regression llm machine-learning neural-networks polynomial-regression python regression regression-models scikitlearn-machine-learning
Last synced: 22 days ago
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My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1:
- Host: GitHub
- URL: https://github.com/nishkarshraj/100daysofmlcode
- Owner: NishkarshRaj
- License: mit
- Created: 2020-01-11T13:40:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-03-18T06:17:27.000Z (3 months ago)
- Last Synced: 2025-04-11T22:38:14.207Z (about 2 months ago)
- Topics: 100daysofcode, 100daysofmlcode, artificial-intelligence, artificial-neural-networks, big-data, classification, classification-algorithm, deep-learning, generative-ai, hacktoberfest, linear-algebra, linear-regression, llm, machine-learning, neural-networks, polynomial-regression, python, regression, regression-models, scikitlearn-machine-learning
- Language: Jupyter Notebook
- Homepage: http://github.com/NishkarshRaj
- Size: 41.2 MB
- Stars: 331
- Watchers: 11
- Forks: 164
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: docs/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
#100DaysofMLCode
## Table of Contents
[**1. Data Pre-processing**](2_Data_Preprocessing/README.md)
* [Importing Libraries](2_Data_Preprocessing/README.md#importing_libraries)
* [Importing Data sets](2_Data_Preprocessing/README.md#importing_datasets)
* [Handling the missing data values](2_Data_Preprocessing/README.md#handling_veracity)
* [Encoding categorical data](2_Data_Preprocessing/README.md#encoding_cat_data)
* [Split Data into Train data and Test data](2_Data_Preprocessing/README.md#split_data)
* [Feature Scaling](2_Data_Preprocessing/README.md#feature_scaling)
[**2. Regression**](3_Regression/README.md)
* [Simple Linear Regression](3_Regression/Simple_Linear_Regression)
* [Multi Linear Regression](3_Regression/Multi_Linear_Regression)
* [Polynomial Regression](3_Regression/Polynomial_Regression)
* [Support Vector Regression](3_Regression/Support_Vector_Regression)
* [Decision Tree Regression](3_Regression/Decision_Tree_Regression)
* [Random Forest Regression](3_Regression/Random_Forest_Regression)
[**3. Classification**](4_Classification/README.md)
* [Logistic Regression](4_Classification/Logistic_Regression)
* [K Nearest Neighbors Classification](4_Classification/K_Nearest_Neighbors)
* [Support Vector Machine](4_Classification/Support_Vector_Machine)
* [Kernel SVM](4_Classification/Kernel-SVM)
* [Naive Bayes](4_Classification/Naive_Bayes)
* [Decision Tree Classification](4_Classification/Decision_Tree_Classification)
* [Random Forest Classification](4_Classification/Random_Forest_Classification)
[**4. Clustering**](5_Clustering/README.md)
* [K-Means Clustering](5_Clustering/K_Means)
* [Hierarchical Clustering](5_Clustering/Hierarchical_Clustering)
[**5. Association Rule**](6_Association_Rule/README.md)
* [Apriori](6_Association_Rule/Apriori)
* [Eclat](6_Association_Rule/Eclat)
[**6. Reinforcement Learning**](7_Reinforcement_Learning/README.md)
* [Upper Confidence Bounds](7_Reinforcement_Learning\Upper_confidence_Bound)
* [Thompson Sampling](7_Reinforcement_Learning/Thompson_Sampling)
[**7. Natural Language Processing** ](8_Natural_Language_Processing)
* [AWS Comprehend](8_Natural_Language_Processing)
[**8. Deep Learning**](9_Deep_Learning/README.md)
* [Artificial Neural Networks (ANN)](9_Deep_Learning/Artificial_Neural_Networks)
* [2. Convolutional Neural Networks (CNN)](9_Deep_Learning/Convolutional_Neural_Networks)
[**9. Dimensionality Reduction**](10_Dimensionality_Reduction/README.md)
* [Principal Component Analysis](10_Dimensionality_Reduction/Principal_Component_Analysis)
* [Linear Discriminant Analysis](10_Dimensionality_Reduction/Linear_Discriminant_Analysis)
* [Kernel PCA](10_Dimensionality_Reduction/Kernel_PCA)
[**10. Model Selection**](11_Model_Selection/README.md)
* [Grid Search](11_Model_Selection/Model_Selection)
* [K-fold Cross Validation](11_Model_Selection/Model_Selection)
* [XGBoost](11_Model_Selection/XGBoost)
**11. Data Visualization**
* Matplotlib library in Python
* Tableau
* Power BI
* Grafana
## Log of my Day-to-Day Activities
Track my daily activities [here](docs/100Days_Log.md)
## How to Contribute
This is an open project and contribution in all forms are welcomed.
Please follow these [Contribution Guidelines](docs/CONTRIBUTING.md)
## Code of Conduct
Adhere to the GitHub specified community [code](docs/CODE_OF_CONDUCT.md).
## License
Check the official MIT License [here](LICENSE).
## 👥 Contributors
- **[@NishkarshRaj](https://github.com/NishkarshRaj)**
- **[@neha-shah99](https://github.com/neha-shah99)**
- **[@manavkapadnis](https://github.com/manavkapadnis)**
- **[@UdayKiranPadhy](https://github.com/UdayKiranPadhy)**
- **[@codingcosmonaut](https://github.com/codingcosmonaut)**
- **[@ekdnam](https://github.com/ekdnam)**
- **[@anushka0301](https://github.com/anushka0301)**
- **[@JeremiahKamama](https://github.com/JeremiahKamama)**
- **[@aenyne](https://github.com/aenyne)**
- **[@pragyakapoor](https://github.com/pragyakapoor)**